Web: http://arxiv.org/abs/2202.04528

Sept. 19, 2022, 1:12 a.m. | Leandro Aparecido Passos, João Paulo Papa, Javier Del Ser, Amir Hussain, Ahsan Adeel

cs.LG updates on arXiv.org arxiv.org

This paper proposes a novel multimodal self-supervised architecture for
energy-efficient audio-visual (AV) speech enhancement that integrates Graph
Neural Networks with canonical correlation analysis (CCA-GNN). The proposed
approach lays its foundations on a state-of-the-art CCA-GNN that learns
representative embeddings by maximizing the correlation between pairs of
augmented views of the same input while decorrelating disconnected features.
The key idea of the conventional CCA-GNN involves discarding
augmentation-variant information and preserving augmentation-invariant
information while preventing capturing of redundant information. Our proposed

arxiv audio canonical energy fusion graph graph neural network information multimodal network neural network speech

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